{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "# data_folder = os.path.join(os.path.expanduser(\"~\"), \"Desktop/Complier/Learning-Data-Mining-with-Python-Second-Edition-master\", \"Chapter05\")\n",
    "# data_folder = \"C:/Users/Anthony Dave/Desktop/Complier/Learning-Data-Mining-with-Python-Second-Edition-master/Chapter05\"\n",
    "# adult_filename = os.path.join(data_folder, \"adult.data\")\n",
    "adult_filename = os.path.join(r\"adult.data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult = pd.read_csv(adult_filename, header=None, names=[\"Age\", \"Work-Class\", \"fnlwgt\", \"Education\",\n",
    "                                                        \"Education-Num\", \"Marital-Status\", \"Occupation\",\n",
    "                                                        \"Relationship\", \"Race\", \"Sex\", \"Capital-gain\",\n",
    "                                                        \"Capital-loss\", \"Hours-per-week\", \"Native-Country\",\n",
    "                                                        \"Earnings-Raw\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult.dropna(how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Work-Class</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-Num</th>\n",
       "      <th>Marital-Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital-gain</th>\n",
       "      <th>Capital-loss</th>\n",
       "      <th>Hours-per-week</th>\n",
       "      <th>Native-Country</th>\n",
       "      <th>Earnings-Raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age         Work-Class  fnlwgt   Education  Education-Num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        Marital-Status          Occupation    Relationship    Race      Sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   Capital-gain  Capital-loss  Hours-per-week  Native-Country Earnings-Raw  \n",
       "0          2174             0              40   United-States        <=50K  \n",
       "1             0             0              13   United-States        <=50K  \n",
       "2             0             0              40   United-States        <=50K  \n",
       "3             0             0              40   United-States        <=50K  \n",
       "4             0             0              40            Cuba        <=50K  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Work-Class', 'fnlwgt', 'Education', 'Education-Num',\n",
       "       'Marital-Status', 'Occupation', 'Relationship', 'Race', 'Sex',\n",
       "       'Capital-gain', 'Capital-loss', 'Hours-per-week', 'Native-Country',\n",
       "       'Earnings-Raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean        40.437456\n",
       "std         12.347429\n",
       "min          1.000000\n",
       "25%         40.000000\n",
       "50%         40.000000\n",
       "75%         45.000000\n",
       "max         99.000000\n",
       "Name: Hours-per-week, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Hours-per-week\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Education-Num\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n",
       "       ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay',\n",
       "       ' Never-worked'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Work-Class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb0fadb1c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(12, 9))\n",
    "sns.swarmplot(x=\"Education-Num\", y=\"Hours-per-week\", hue=\"Earnings-Raw\", data=adult[::50], size=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.arange(30).reshape((10, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14],\n",
       "       [15, 16, 17],\n",
       "       [18, 19, 20],\n",
       "       [21, 22, 23],\n",
       "       [24, 25, 26],\n",
       "       [27, 28, 29]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:,1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  1,  5],\n",
       "       [ 6,  1,  8],\n",
       "       [ 9,  1, 11],\n",
       "       [12,  1, 14],\n",
       "       [15,  1, 17],\n",
       "       [18,  1, 20],\n",
       "       [21,  1, 23],\n",
       "       [24,  1, 26],\n",
       "       [27,  1, 29]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "vt = VarianceThreshold()\n",
    "Xt = vt.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2],\n",
       "       [ 3,  5],\n",
       "       [ 6,  8],\n",
       "       [ 9, 11],\n",
       "       [12, 14],\n",
       "       [15, 17],\n",
       "       [18, 20],\n",
       "       [21, 23],\n",
       "       [24, 26],\n",
       "       [27, 29]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.  0. 27.]\n"
     ]
    }
   ],
   "source": [
    "print(vt.variances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = adult[[\"Age\", \"Education-Num\", \"Capital-gain\", \"Capital-loss\", \"Hours-per-week\"]].values\n",
    "y = (adult[\"Earnings-Raw\"] == ' >50K').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "transformer = SelectKBest(score_func=chi2, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.60061182e+03 2.40142178e+03 8.21924671e+07 1.37214589e+06\n",
      " 6.47640900e+03]\n"
     ]
    }
   ],
   "source": [
    "Xt_chi2 = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "def multivariate_pearsonr(X, y):\n",
    "    scores, pvalues = [], []\n",
    "    for column in range(X.shape[1]):\n",
    "        cur_score, cur_p = pearsonr(X[:,column], y)\n",
    "        scores.append(abs(cur_score))\n",
    "        pvalues.append(cur_p)\n",
    "    return (np.array(scores), np.array(pvalues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2340371  0.33515395 0.22332882 0.15052631 0.22968907]\n"
     ]
    }
   ],
   "source": [
    "transformer = SelectKBest(score_func=multivariate_pearsonr, k=3)\n",
    "Xt_pearson = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "clf = DecisionTreeClassifier(random_state=14)\n",
    "scores_chi2 = cross_val_score(clf, Xt_chi2, y, scoring='accuracy')\n",
    "scores_pearson = cross_val_score(clf, Xt_pearson, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi2 performance: 0.829\n",
      "Pearson performance: 0.772\n"
     ]
    }
   ],
   "source": [
    "print(\"Chi2 performance: {0:.3f}\".format(scores_chi2.mean()))\n",
    "print(\"Pearson performance: {0:.3f}\".format(scores_pearson.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.utils import as_float_array\n",
    "\n",
    "class MeanDiscrete(TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        X = as_float_array(X)\n",
    "        self.mean = np.mean(X, axis=0)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = as_float_array(X)\n",
    "        assert X.shape[1] == self.mean.shape[0]\n",
    "        return X > self.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_discrete = MeanDiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = mean_discrete.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from numpy.testing import assert_array_equal\n",
    "\n",
    "def test_meandiscrete():\n",
    "    X_test = np.array([[ 0,  2],\n",
    "                        [ 3,  5],\n",
    "                        [ 6,  8],\n",
    "                        [ 9, 11],\n",
    "                        [12, 14],\n",
    "                        [15, 17],\n",
    "                        [18, 20],\n",
    "                        [21, 23],\n",
    "                        [24, 26],\n",
    "                        [27, 29]])\n",
    "    mean_discrete = MeanDiscrete()\n",
    "    mean_discrete.fit(X_test)\n",
    "    assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))\n",
    "    X_transformed = mean_discrete.transform(X_test)\n",
    "    X_expected = np.array([[ 0,  0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1]])\n",
    "    assert_array_equal(X_transformed, X_expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "test_meandiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([('mean_discrete', MeanDiscrete()),\n",
    "                     ('classifier', DecisionTreeClassifier(random_state=14))])\n",
    "scores_mean_discrete = cross_val_score(pipeline, X, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Discrete performance: 0.803\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Discrete performance: {0:.3f}\".format(scores_mean_discrete.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
